Professor CHEN, Jinsong

RESEARCH INTERESTS

My research interests center around two main areas. The first area focuses on latent variable modeling and structural equation modeling. I am currently developing a partially confirmatory approach to both frameworks with multiple research directions and potential applications. The estimation algorithm presents a significant challenge that I aim to overcome.

The second area is the integration of psychometrics with NLP. I am exploring different approaches to combine large language models with psychometrics, which can be applied in various educational and psychological domains. One potential application I am investigating is to improve STEM learning with NLP-generated educational contents such as knowledge, solutions, and examples.

TEACHING AREA

• Quantitative methods, psychometrics, and statistics

Title: A Partially Confirmatory Approach to Factor Analysis with Bayesian Learning

 (*corresponding author)

• Chen, J. (2022). Fully and partially exploratory factor analysis with bi-level Bayesian regularization. Behavior Research Methods. DOI: 10.3758/s13428-022-01884-7.

• Ma W., Chen J. & Jiang Z. (2022). Attribute continuity in cognitive diagnosis models: impact on parameter estimation and its detection, Behaviormetrika. DOI: 10.1007/s41237-022-00174-y.
• Chen, J. (2022). Partially confirmatory approach to factor analysis with Bayesian learning: A LAWBL tutorial. Structural Equation Modeling: A Multidisciplinary Journal. 29, 800-816. DOI: 10.1080/10705511.2022.2039660.
• Chen, J. (2021). A generalized partially confirmatory factor analysis framework with mixed Bayesian Lasso methods. Multivariate Behavioral Research. DOI: 10.1080/00273171.2021.1925520.
• Chen, J. (2021). A Bayesian regularized approach to exploratory factor analysis in one step. Structural Equation Modeling: A Multidisciplinary Journal, 28(4), 518-528. DOI: 10.1080/10705511.2020.1854763.
• Chen, J. (2020). A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso. Psychometrika. 85(3), 738-774. DOI: 10.1007/s11336-020-09724-3.
• *Chen, J., Guo, Z., Zhang, L., & Pan, J. (2021). A partially confirmatory approach to scale development with the Bayesian Lasso. Psychological Methods, 26(2), 210–235. DOI: 10.1037/met0000293.
• *Chen, J., Li, L., & Zhang, D. (2020). Students with specific difficulties in Geometry: Exploring the TIMSS 2011 data with plausible values and latent profile analysis. Learning Disability Quarterly. 44(1), 11-22. DOI: 10.1177/0731948720905099.
• Choi, J., Chen, J., & Harring, J. (2020). Logistic growth modeling with Markov chain Monte Carlo estimation. Journal of Modern Applied Statistical Methods, 18(1), eP2997. DOI: 10.22237/jmasm/1556669820.
• Guo, Z., & *Chen, J. (2019). Teaching evaluation under the view of modern validity: Reflection and suggestions [Chinese]. Higher Education Exploration. 3, 11-15.
• *Chen, J., & de la Torre, J. (2018). Introducing the general polytomous diagnosis modeling framework. Front. Psychol. 9:1474. DOI: 10.3389/fpsyg.2018.01474
• Chen, J. (2018) A hierarchical taxonomy of test validity for more flexibility of validation. Front. Psychol. 9:972. DOI: 10.3389/fpsyg.2018.00972
• Lin, Y., Chen, H., & *Chen, J. (2018). Exploring cognitive diagnosis retrofitting and further analyses of language proficiency testing: The case of the Guangzhou English achievement examination. Journal of Psychological Science [Chinese]. 41(3), 1-7.
• Zhang, G., Zhou, X., Chen, J., Gong, Y., Sun, S., Yu, X. (2018). The Viral Impact of Emotion on Social Transmission under Control Context. Biomed J Sci & Tech Res. 7(3). DOI: 10.26717/ BJSTR.2018.07.001499.
• Chen, H., & Chen, J. (2017). Cognitive diagnostic research on Chinese students’ English listening skills and implications on skill training. English language Teaching. 12(10), 107-115.
• *Chen, J., & Zhou H. (2017). Test designs and modeling under the general nominal diagnosis model framework. PLOS One, 12(6): e0180016. https://doi.org/10.1371/journal.pone.0180016
• Chen, J. (2017). A residual-based approach to validate Q-matrix specifications. Applied Psychological Measurement, 41(4), 277–293. https://doi.org/10.1177/0146621616686021
• Chen, J. (2017). Advancing the Bayesian approach for multidimensional polytomous and nominal IRT models: Model formulations and fit measures. Applied Psychological Measurement, 41, 3-16. https://doi.org/10.1177/0146621616669096
• Chen, J. (2017). Introducing a flexible approach to test validity based on context-specific construct. Theory and Psychology, 27(5), 711-718. https://doi.org/10.1177/0959354317722866
• Zhang, D., Ding, Y., Lee, S., & *Chen, J. (2017). Strategic development of multiplication problem solving: Patterns of students’ strategy choices. The Journal of Educational Research, 110, 159-170. https://doi.org/10.1080/00220671.2015.1060928
• Chen, H., & *Chen, J. (2016). Retrofitting non-cognitive-diagnostic reading assessment under the generalized DINA model framework. Language Assessment Quarterly, 13, 218-230. https://doi.org/10.1080/15434303.2016.1210610
• Chen, H., & *Chen, J. (2016). Exploring reading comprehension skill relationships through the G-DINA model. Educational Psychology: An International Journal of Experimental Educational Psychology, 36, 1049-1064. http://dx.doi.org/10.1080/01443410.2015.1076764
• *Chen, J., Zhang, D., & Choi, J. (2015). Estimation of the latent mediated effect with ordinal data using the limited-information and Bayesian full-information approaches. Behavior Research Methods, 47, 1260-1273. https://doi.org/10.3758/s13428-014-0526-3
• *Chen, J., & de la Torre, J. (2014). A procedure for diagnostically modeling extant large-scale assessment data: The case of the Programme for International Student Assessment in Reading. Psychology, 5, 1967-1978. https://doi.org/10.4236/psych.2014.518200
• *Chen, J., Choi, J., Weiss, B. A., & Stapleton, L. (2014). An empirical evaluation of mediation effect analysis with manifest and latent variables using Morkov chain Monte Carlo and alternative estimation methods. Structural Equation Modeling, 21, 253-262. https://doi.org/10.1080/10705511.2014.882688
• *Chen, J., & de la Torre, J. (2013). A general cognitive diagnosis model for expert-defined polytomous attributes. Applied Psychological Measurement, 37, 419-437. https://doi.org/10.1177/0146621613479818
• *Chen, J., de la Torre, J., & Zhang, Z. (2013). Relative and absolute fit evaluation in cognitive diagnosis modeling. Journal of Educational Measurement, 50, 123-140. https://doi.org/10.1111/j.1745-3984.2012.00185.x
• Chen, H., & Chen, J. (2013). Validating G-DINA model in language test diagnosis. Journal of Psychological Science [Chinese], 36, 1470-1475.
• Choi, J., Kim, S., Chen, J., & Dannels, A. S. (2011). A comparison of maximum likelihood and Bayesian estimation for polychoric correlation using Monte Carlo simulation. Journal of Educational and Behavioral Statistics, 36(4), 523-549. https://doi.org/10.3102/1076998610381398
• Choi, J., Dunlop, M., Chen, J., & Kim, S. (2011). A comparison of different approaches for coefficient alpha for ordinal data. Journal of Educational Evaluation, 24(2), 485-506.
• Kroopnick, H. M., Chen, J., Choi, J. M., & Dayton, C. M. (2010). Assessing classification bias in latent class analysis: Comparing resubstitution and leave-one-out methods. Journal of Modern Applied Statistical Methods, 9(1), 52-63.
• *Chen, J., & Choi, J., (2009). A comparison of maximum likelihood and expected a posteriori estimation for polychoric correlation using Monte Carlo simulation. Journal of Modern Applied Statistical Methods, 8(1), 337-354.
• Zhou, X.-Y., Lei, Q., Marley, S. C., & Chen, J. (2009). The existential function of babies: Babies as a buffer of death-related anxiety. Asian Journal of Social Psychology, 12(1), 40–46. https://doi.org/10.1111/j.1467-839X.2008.01268.x

Computer programs distributed

• EAPPCC: A Matlab program for estimating polychoric correlation matrices
• GCDM: An Ox program for the general cognitive diagnosis modeling framework with dichotomous and polytomous attributes and dichotomous responses
• GRDM: An Ox program for the graded response diagnosis modeling framework
• GNDM: An Ox program for the general nominal diagnosis modeling framework
• LAWBL: Latent (variable) analysis with Bayesian learning (R package version 1.3.0). Retrieved from https://CRAN.R-project.org/package=LAWBL

Keynotes or invited talks

• 时间: 2015.12; 内容: 全球華人教育資訊與評估學術研討會暨中國測驗學會年會；主办单位：台中教育大学；地点：台湾；身份：报告嘉宾
• Time: 2015.12; Content: Public Lecture & Academic Workshop on Cognitive Diagnosis Modeling; Hosted by: The Hong Kong Institute of Education; Venue: Hong Kong; Role: Speaker
• Time: 2017.03; Content: Triangle Framework for Educational and Psychological Testing and Measurement; Hosted by: The University of Hong Kong; Venue: Hong Kong; Role: Speaker
• 时间: 2018.01; 内容: 第二届全国心理学专业《心理统计学》《心理测量学》任课教师培训班；主办单位：中国心理学会；地点：广州；身份：课程报告人
• Time: 2018.05; Content: Improving diagnostic assessments with Bayesian regularized item response models; Hosted by: Shanghai International Studies University; Venue: Shanghai; Role: Speaker
• 时间: 2018.06; 内容:智慧评价与学习学术研讨会；主办单位：江西师范大学；地点：南昌；身份：报告嘉宾
• Time: 2018.12; Content: Partially Confirmatory Approach to Scale Development with Bayesian Lasso; Hosted by: the Education University of Hong Kong; Venue: Hong Kong; Role: Speaker
• 时间: 2019.1 内容: 第五届中国教育财政学术研讨会暨2019年中国教育发展战略学会教育财政专业委员会年会; 主办单位：北京大学；地点：北京；身份：特邀嘉宾